As to tea resources in Zhejiang Province at present, there are 8 kinds of national geographical indication products, 23 national geographical indication trademarks, and 7 kinds of national geographical indication of a...As to tea resources in Zhejiang Province at present, there are 8 kinds of national geographical indication products, 23 national geographical indication trademarks, and 7 kinds of national geographical indication of agricultural products. From the geographical indication protection, geographical indication trademark registration, geographical indication registration of agricultural products, we conduct a analysis on the current protection of geographical indication intellectual property of tea in Zhejiang Province, and put forth the following countermeasures: (i) Based on the relevant tangible cultural heritage and natural heritage, conducting in-depth study on the characteristics of natural factors and human factors concerning geographical indication of famous tea; (ii) Based on the protection pattern of national geographical indication products, registering the national geographical indication trademarks, and registering the national agricultural product geographical indication; (iii) Taking full advantage of special mark of geographical indication products and agricultural brand heritage, and integrating the tea brands within the scope of protection of geographical indication; (iv) Exploiting and arranging the intangible cultural heritage related to tea, strengthening the intangible cultural heritage protection of tea in the province, and endeavoring to include Longjing tea in the world's intangible cultural heritage list on traditional craftsmanship of green tea.展开更多
Fluorine content of tea leaves in mountainy tea gardens in Zhejiang Province and the influence factors were measured in 68 random plots,and a soil sample,a Spring’s tea sample and an Autumn’s tea sample were collect...Fluorine content of tea leaves in mountainy tea gardens in Zhejiang Province and the influence factors were measured in 68 random plots,and a soil sample,a Spring’s tea sample and an Autumn’s tea sample were collected in each plot.The results showed that 99.3% of the total tea samples met the requirement of NY659-2003 in respect of fluorine,and the mean content of all 136 samples was much lower than the average fluorine level of Chinese green tea.The leaf fluorine contents of in the Spring and Autumn tea were respectively(60.28±47.00)and(61.43±31.19)mg·kg-1 and there was no statistical difference between them.Regression analyses on the fluorine contents of tea and soil indicated that the fluorine content in soil significantly affect the fluorine content in Spring’s tea but not that in Autumn’s tea.The fluorine content of tea leaves was not different in relation to the altitude of tea gardens.However,the fluorine content was significantly(P<0.01)different among various varieties of tea plants(Camellia sinensis),which suggested the capabilities for them to absorb and accumulate fluorine from their environment were different.展开更多
为了鉴别西湖龙井和浙江龙井茶叶,采用近红外光谱分析技术结合化学计量学方法建立了识别模型。先对原始光谱进行标准正态变换(Standard Normal Variant,SNV)预处理,然后分别采用最小二乘判别分析(Partial Least Square Regression-discr...为了鉴别西湖龙井和浙江龙井茶叶,采用近红外光谱分析技术结合化学计量学方法建立了识别模型。先对原始光谱进行标准正态变换(Standard Normal Variant,SNV)预处理,然后分别采用最小二乘判别分析(Partial Least Square Regression-discriminantAnalysis,PLS-DA)、最小二乘支持向量机(Least Square Support Vector Machine,LSSVM)和径向基人工神经网络(Radial Basis Function Neural Network,RBFNN)三种模型对西湖龙井和浙江龙井茶叶进行预测。最小二乘支持向量机参数通过网格搜索和完全交叉验证得到优化。经优化后,惩罚系数(γ)和核函数参数(δ~2)分别为229.1和124.9;RBFNN最佳隐藏层神经元个数为27个。通过比较可知,LSSVM的预测性能最好,其校正集均方根误差(RMSECV)和相关系数(R^2)分别为0和1,验证集均方根误差(RMSEP)和相关系数(R^2)也分别为0和1,分辨正确率为100%。展开更多
基金Supported by Humanities and Social Sciences Planning Fund Project,the Ministry of Education (11YJA850019)Scientific and Technological Innovation Project,the Ministry of Culture (2011021)+2 种基金Hubei Social Science Fund Project (2010274) Science and Technology Research Project,Hubei Provincial Department of Education (B20112805)Humanities and Social Sciences Project,Hubei Provincial Department of Education (2011jytq165)
文摘As to tea resources in Zhejiang Province at present, there are 8 kinds of national geographical indication products, 23 national geographical indication trademarks, and 7 kinds of national geographical indication of agricultural products. From the geographical indication protection, geographical indication trademark registration, geographical indication registration of agricultural products, we conduct a analysis on the current protection of geographical indication intellectual property of tea in Zhejiang Province, and put forth the following countermeasures: (i) Based on the relevant tangible cultural heritage and natural heritage, conducting in-depth study on the characteristics of natural factors and human factors concerning geographical indication of famous tea; (ii) Based on the protection pattern of national geographical indication products, registering the national geographical indication trademarks, and registering the national agricultural product geographical indication; (iii) Taking full advantage of special mark of geographical indication products and agricultural brand heritage, and integrating the tea brands within the scope of protection of geographical indication; (iv) Exploiting and arranging the intangible cultural heritage related to tea, strengthening the intangible cultural heritage protection of tea in the province, and endeavoring to include Longjing tea in the world's intangible cultural heritage list on traditional craftsmanship of green tea.
文摘Fluorine content of tea leaves in mountainy tea gardens in Zhejiang Province and the influence factors were measured in 68 random plots,and a soil sample,a Spring’s tea sample and an Autumn’s tea sample were collected in each plot.The results showed that 99.3% of the total tea samples met the requirement of NY659-2003 in respect of fluorine,and the mean content of all 136 samples was much lower than the average fluorine level of Chinese green tea.The leaf fluorine contents of in the Spring and Autumn tea were respectively(60.28±47.00)and(61.43±31.19)mg·kg-1 and there was no statistical difference between them.Regression analyses on the fluorine contents of tea and soil indicated that the fluorine content in soil significantly affect the fluorine content in Spring’s tea but not that in Autumn’s tea.The fluorine content of tea leaves was not different in relation to the altitude of tea gardens.However,the fluorine content was significantly(P<0.01)different among various varieties of tea plants(Camellia sinensis),which suggested the capabilities for them to absorb and accumulate fluorine from their environment were different.
文摘为了鉴别西湖龙井和浙江龙井茶叶,采用近红外光谱分析技术结合化学计量学方法建立了识别模型。先对原始光谱进行标准正态变换(Standard Normal Variant,SNV)预处理,然后分别采用最小二乘判别分析(Partial Least Square Regression-discriminantAnalysis,PLS-DA)、最小二乘支持向量机(Least Square Support Vector Machine,LSSVM)和径向基人工神经网络(Radial Basis Function Neural Network,RBFNN)三种模型对西湖龙井和浙江龙井茶叶进行预测。最小二乘支持向量机参数通过网格搜索和完全交叉验证得到优化。经优化后,惩罚系数(γ)和核函数参数(δ~2)分别为229.1和124.9;RBFNN最佳隐藏层神经元个数为27个。通过比较可知,LSSVM的预测性能最好,其校正集均方根误差(RMSECV)和相关系数(R^2)分别为0和1,验证集均方根误差(RMSEP)和相关系数(R^2)也分别为0和1,分辨正确率为100%。
